Last checked: 31-Jan-2022
21 participants (experimental error = 1) >> 20 participants were included for analysis.
Actual instruction:
Each performance was produced in order to either 1) teach the musical expressive technique (as a teacher) or 2) perform their best (as a performer).
You will be asked to judge whether each performer had the intention to teach or not by pressing the 'Yes' <Left> or 'No' <Right> key.
***: 0.001, **: 0.01, *: 0.05## `geom_smooth()` using formula 'y ~ x'
# normality check
ioi_art_norm_teaching <- shapiro.test(ioi[Skill == "articulation"]$Teaching)
ioi_art_norm_judge <- shapiro.test(ioi[Skill == "articulation"]$Mean)
qqnorm(ioi[Skill == "articulation"]$Teaching)
qqline(ioi[Skill == "articulation"]$Teaching)
ioi_art_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: ioi[Skill == "articulation"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(ioi[Skill == "articulation"]$Mean)
qqline(ioi[Skill == "articulation"]$Mean)
ioi_art_norm_judge
##
## Shapiro-Wilk normality test
##
## data: ioi[Skill == "articulation"]$Mean
## W = 0.98238, p-value = 0.6803
cor_ioi_art <- cor.test(ioi[Skill == "articulation"]$Teaching, ioi[Skill == "articulation"]$Mean)
cor_ioi_art
##
## Pearson's product-moment correlation
##
## data: ioi[Skill == "articulation"]$Teaching and ioi[Skill == "articulation"]$Mean
## t = 8.2739, df = 46, p-value = 1.171e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6270180 0.8669915
## sample estimates:
## cor
## 0.7733708
# normality check
ioi_dyn_norm_teaching <- shapiro.test(ioi[Skill == "dynamics"]$Teaching)
ioi_dyn_norm_judge <- shapiro.test(ioi[Skill == "dynamics"]$Mean)
qqnorm(ioi[Skill == "dynamics"]$Teaching)
qqline(ioi[Skill == "dynamics"]$Teaching)
ioi_dyn_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: ioi[Skill == "dynamics"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(ioi[Skill == "dynamics"]$Mean)
qqline(ioi[Skill == "dynamics"]$Mean)
ioi_dyn_norm_judge
##
## Shapiro-Wilk normality test
##
## data: ioi[Skill == "dynamics"]$Mean
## W = 0.94923, p-value = 0.03727
cor_ioi_dyn <- cor.test(ioi[Skill == "dynamics"]$Teaching, ioi[Skill == "dynamics"]$Mean)
cor_ioi_dyn
##
## Pearson's product-moment correlation
##
## data: ioi[Skill == "dynamics"]$Teaching and ioi[Skill == "dynamics"]$Mean
## t = 3.151, df = 46, p-value = 0.00286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1558612 0.6300448
## sample estimates:
## cor
## 0.4213368
cor_ioi_dyn_spearman <- cor.test(ioi[Skill == "dynamics"]$Teaching, ioi[Skill == "dynamics"]$Mean, method = "spearman", exact = FALSE)
cor_ioi_dyn_spearman
##
## Spearman's rank correlation rho
##
## data: ioi[Skill == "dynamics"]$Teaching and ioi[Skill == "dynamics"]$Mean
## S = 13487, p-value = 0.06555
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2679796
## `geom_smooth()` using formula 'y ~ x'
# normality check
ioi_art_tra_norm_teaching <- shapiro.test(ioi_tra[Skill == "articulation"]$Teaching)
ioi_art_tra_norm_judge <- shapiro.test(ioi_tra[Skill == "articulation"]$Mean)
qqnorm(ioi_tra[Skill == "articulation"]$Teaching)
qqline(ioi_tra[Skill == "articulation"]$Teaching)
ioi_art_tra_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: ioi_tra[Skill == "articulation"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(ioi_tra[Skill == "articulation"]$Mean)
qqline(ioi_tra[Skill == "articulation"]$Mean)
ioi_art_tra_norm_judge
##
## Shapiro-Wilk normality test
##
## data: ioi_tra[Skill == "articulation"]$Mean
## W = 0.9642, p-value = 0.1492
cor.test(ioi_tra[Skill == "articulation"]$Teaching, ioi_tra[Skill == "articulation"]$Mean)
##
## Pearson's product-moment correlation
##
## data: ioi_tra[Skill == "articulation"]$Teaching and ioi_tra[Skill == "articulation"]$Mean
## t = 6.4766, df = 46, p-value = 5.572e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5057195 0.8148547
## sample estimates:
## cor
## 0.6906176
# normality check
ioi_dyn_tra_norm_teaching <- shapiro.test(ioi_tra[Skill == "dynamics"]$Teaching)
ioi_dyn_tra_norm_judge <- shapiro.test(ioi_tra[Skill == "dynamics"]$Mean)
qqnorm(ioi_tra[Skill == "dynamics"]$Teaching)
qqline(ioi_tra[Skill == "dynamics"]$Teaching)
ioi_dyn_tra_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: ioi_tra[Skill == "dynamics"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(ioi_tra[Skill == "dynamics"]$Mean)
qqline(ioi_tra[Skill == "dynamics"]$Mean)
ioi_dyn_tra_norm_judge
##
## Shapiro-Wilk normality test
##
## data: ioi_tra[Skill == "dynamics"]$Mean
## W = 0.884, p-value = 0.0001991
cor.test(ioi_tra[Skill == "dynamics"]$Teaching, ioi_tra[Skill == "dynamics"]$Mean)
##
## Pearson's product-moment correlation
##
## data: ioi_tra[Skill == "dynamics"]$Teaching and ioi_tra[Skill == "dynamics"]$Mean
## t = 3.1131, df = 46, p-value = 0.003181
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1509028 0.6269728
## sample estimates:
## cor
## 0.4171514
cor.test(ioi_tra[Skill == "dynamics"]$Teaching, ioi_tra[Skill == "dynamics"]$Mean, method = "spearman", exact = FALSE)
##
## Spearman's rank correlation rho
##
## data: ioi_tra[Skill == "dynamics"]$Teaching and ioi_tra[Skill == "dynamics"]$Mean
## S = 10569, p-value = 0.002514
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4263485
## `geom_smooth()` using formula 'y ~ x'
## Articulation (n.s.)
# normality check
cv_art_norm_teaching <- shapiro.test(cv[Skill == "articulation"]$Teaching)
cv_art_norm_judge <- shapiro.test(cv[Skill == "articulation"]$CV)
qqnorm(cv[Skill == "articulation"]$Teaching)
qqline(cv[Skill == "articulation"]$Teaching)
cv_art_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: cv[Skill == "articulation"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(cv[Skill == "articulation"]$CV)
qqline(cv[Skill == "articulation"]$CV)
cv_art_norm_judge
##
## Shapiro-Wilk normality test
##
## data: cv[Skill == "articulation"]$CV
## W = 0.6622, p-value = 3.027e-09
cor.test(cv[Skill == "articulation"]$Teaching, cv[Skill == "articulation"]$CV)
##
## Pearson's product-moment correlation
##
## data: cv[Skill == "articulation"]$Teaching and cv[Skill == "articulation"]$CV
## t = -1.7368, df = 46, p-value = 0.08912
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.49716450 0.03879628
## sample estimates:
## cor
## -0.248073
cor.test(cv[Skill == "articulation"]$Teaching, cv[Skill == "articulation"]$CV, method = "spearman", exact = FALSE)
##
## Spearman's rank correlation rho
##
## data: cv[Skill == "articulation"]$Teaching and cv[Skill == "articulation"]$CV
## S = 23645, p-value = 0.05096
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.28338
# normality check
cv_dyn_norm_teaching <- shapiro.test(cv[Skill == "dynamics"]$Teaching)
cv_dyn_norm_judge <- shapiro.test(cv[Skill == "dynamics"]$CV)
qqnorm(cv[Skill == "dynamics"]$Teaching)
qqline(cv[Skill == "dynamics"]$Teaching)
cv_dyn_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: cv[Skill == "dynamics"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(cv[Skill == "dynamics"]$CV)
qqline(cv[Skill == "dynamics"]$CV)
cv_dyn_norm_judge
##
## Shapiro-Wilk normality test
##
## data: cv[Skill == "dynamics"]$CV
## W = 0.86123, p-value = 4.381e-05
cor.test(cv[Skill == "dynamics"]$Teaching, cv[Skill == "dynamics"]$CV)
##
## Pearson's product-moment correlation
##
## data: cv[Skill == "dynamics"]$Teaching and cv[Skill == "dynamics"]$CV
## t = -0.53753, df = 46, p-value = 0.5935
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3551678 0.2098392
## sample estimates:
## cor
## -0.07900602
cor.test(cv[Skill == "dynamics"]$Teaching, cv[Skill == "dynamics"]$CV, method = "spearman", exact = FALSE)
##
## Spearman's rank correlation rho
##
## data: cv[Skill == "dynamics"]$Teaching and cv[Skill == "dynamics"]$CV
## S = 19384, p-value = 0.7252
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.05208142
## `geom_smooth()` using formula 'y ~ x'
# normality check
kot_leg_norm_teaching <- shapiro.test(kot_all[Subcomponent == "Legato"]$Teaching)
kot_leg_norm_judge <- shapiro.test(kot_all[Subcomponent == "Legato"]$Mean)
qqnorm(kot_all[Subcomponent == "Legato"]$Teaching)
qqline(kot_all[Subcomponent == "Legato"]$Teaching)
kot_leg_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Legato"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(kot_all[Subcomponent == "Legato"]$Mean)
qqline(kot_all[Subcomponent == "Legato"]$Mean)
kot_leg_norm_judge
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Legato"]$Mean
## W = 0.97678, p-value = 0.4526
cor_kot_leg <- cor.test(kot_all[Subcomponent == "Legato"]$Teaching, kot_all[Subcomponent == "Legato"]$Mean)
cor_kot_leg
##
## Pearson's product-moment correlation
##
## data: kot_all[Subcomponent == "Legato"]$Teaching and kot_all[Subcomponent == "Legato"]$Mean
## t = 2.9349, df = 46, p-value = 0.005192
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1273759 0.6121920
## sample estimates:
## cor
## 0.3971374
# normality check
kot_sta_norm_teaching <- shapiro.test(kot_all[Subcomponent == "Staccato"]$Teaching)
kot_sta_norm_judge <- shapiro.test(kot_all[Subcomponent == "Staccato"]$Mean)
qqnorm(kot_all[Subcomponent == "Staccato"]$Teaching)
qqline(kot_all[Subcomponent == "Staccato"]$Teaching)
kot_sta_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Staccato"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(kot_all[Subcomponent == "Staccato"]$Mean)
qqline(kot_all[Subcomponent == "Staccato"]$Mean)
kot_sta_norm_judge
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Staccato"]$Mean
## W = 0.95441, p-value = 0.06005
cor_kot_sta <- cor.test(kot_all[Subcomponent == "Staccato"]$Teaching, kot_all[Subcomponent == "Staccato"]$Mean)
cor_kot_sta
##
## Pearson's product-moment correlation
##
## data: kot_all[Subcomponent == "Staccato"]$Teaching and kot_all[Subcomponent == "Staccato"]$Mean
## t = -7.3302, df = 46, p-value = 2.919e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.8424434 -0.5684184
## sample estimates:
## cor
## -0.7340057
# normality check
kot_for_norm_teaching <- shapiro.test(kot_all[Subcomponent == "Forte"]$Teaching)
kot_for_norm_judge <- shapiro.test(kot_all[Subcomponent == "Forte"]$Mean)
qqnorm(kot_all[Subcomponent == "Forte"]$Teaching)
qqline(kot_all[Subcomponent == "Forte"]$Teaching)
kot_for_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Forte"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(kot_all[Subcomponent == "Forte"]$Mean)
qqline(kot_all[Subcomponent == "Forte"]$Mean)
kot_for_norm_judge
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Forte"]$Mean
## W = 0.76648, p-value = 2.454e-07
cor_kot_for <- cor.test(kot_all[Subcomponent == "Forte"]$Teaching, kot_all[Subcomponent == "Forte"]$Mean)
cor_kot_for
##
## Pearson's product-moment correlation
##
## data: kot_all[Subcomponent == "Forte"]$Teaching and kot_all[Subcomponent == "Forte"]$Mean
## t = -0.24099, df = 46, p-value = 0.8106
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3164515 0.2511592
## sample estimates:
## cor
## -0.03550962
cor_kot_for_spearman <- cor.test(kot_all[Subcomponent == "Forte"]$Teaching, kot_all[Subcomponent == "Forte"]$Mean, method = "spearman", exact = FALSE)
cor_kot_for_spearman
##
## Spearman's rank correlation rho
##
## data: kot_all[Subcomponent == "Forte"]$Teaching and kot_all[Subcomponent == "Forte"]$Mean
## S = 15680, p-value = 0.3123
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1489441
# normality check
kot_pia_norm_teaching <- shapiro.test(kot_all[Subcomponent == "Piano"]$Teaching)
kot_pia_norm_judge <- shapiro.test(kot_all[Subcomponent == "Piano"]$Mean)
qqnorm(kot_all[Subcomponent == "Piano"]$Teaching)
qqline(kot_all[Subcomponent == "Piano"]$Teaching)
kot_pia_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Piano"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(kot_all[Subcomponent == "Piano"]$Mean)
qqline(kot_all[Subcomponent == "Piano"]$Mean)
kot_pia_norm_judge
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Piano"]$Mean
## W = 0.71858, p-value = 2.869e-08
cor_kot_pia <- cor.test(kot_all[Subcomponent == "Piano"]$Teaching, kot_all[Subcomponent == "Piano"]$Mean)
cor_kot_pia
##
## Pearson's product-moment correlation
##
## data: kot_all[Subcomponent == "Piano"]$Teaching and kot_all[Subcomponent == "Piano"]$Mean
## t = -1.3414, df = 46, p-value = 0.1864
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.45317378 0.09537053
## sample estimates:
## cor
## -0.1940218
cor_kot_pia_spearman <- cor.test(kot_all[Subcomponent == "Piano"]$Teaching, kot_all[Subcomponent == "Piano"]$Mean, method = "spearman", exact = FALSE)
cor_kot_pia_spearman
##
## Spearman's rank correlation rho
##
## data: kot_all[Subcomponent == "Piano"]$Teaching and kot_all[Subcomponent == "Piano"]$Mean
## S = 20595, p-value = 0.4251
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1178369
## `geom_smooth()` using formula 'y ~ x'
# normality check
vel_for_norm_teaching <- shapiro.test(vel_all[Subcomponent == "Forte"]$Teaching)
vel_for_norm_judge <- shapiro.test(vel_all[Subcomponent == "Forte"]$Mean)
qqnorm(vel_all[Subcomponent == "Forte"]$Teaching)
qqline(vel_all[Subcomponent == "Forte"]$Teaching)
vel_for_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Forte"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(vel_all[Subcomponent == "Forte"]$Mean)
qqline(vel_all[Subcomponent == "Forte"]$Mean)
vel_for_norm_judge
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Forte"]$Mean
## W = 0.97045, p-value = 0.2636
cor_vel_for <- cor.test(vel_all[Subcomponent == "Forte"]$Teaching, vel_all[Subcomponent == "Forte"]$Mean)
cor_vel_for
##
## Pearson's product-moment correlation
##
## data: vel_all[Subcomponent == "Forte"]$Teaching and vel_all[Subcomponent == "Forte"]$Mean
## t = 3.439, df = 46, p-value = 0.001251
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1928879 0.6525249
## sample estimates:
## cor
## 0.4522372
# normality check
vel_pia_norm_teaching <- shapiro.test(vel_all[Subcomponent == "Piano"]$Teaching)
vel_pia_norm_judge <- shapiro.test(vel_all[Subcomponent == "Piano"]$Mean)
qqnorm(vel_all[Subcomponent == "Piano"]$Teaching)
qqline(vel_all[Subcomponent == "Piano"]$Teaching)
vel_pia_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Piano"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(vel_all[Subcomponent == "Piano"]$Mean)
qqline(vel_all[Subcomponent == "Piano"]$Mean)
vel_pia_norm_judge
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Piano"]$Mean
## W = 0.98827, p-value = 0.9089
cor_vel_pia <- cor.test(vel_all[Subcomponent == "Piano"]$Teaching, vel_all[Subcomponent == "Piano"]$Mean)
cor_vel_pia
##
## Pearson's product-moment correlation
##
## data: vel_all[Subcomponent == "Piano"]$Teaching and vel_all[Subcomponent == "Piano"]$Mean
## t = -1.5371, df = 46, p-value = 0.1311
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.47531585 0.06733088
## sample estimates:
## cor
## -0.2210324
# normality check
vel_leg_norm_teaching <- shapiro.test(vel_all[Subcomponent == "Legato"]$Teaching)
vel_leg_norm_judge <- shapiro.test(vel_all[Subcomponent == "Legato"]$Mean)
qqnorm(vel_all[Subcomponent == "Legato"]$Teaching)
qqline(vel_all[Subcomponent == "Legato"]$Teaching)
vel_leg_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Legato"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(vel_all[Subcomponent == "Legato"]$Mean)
qqline(vel_all[Subcomponent == "Legato"]$Mean)
vel_leg_norm_judge
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Legato"]$Mean
## W = 0.91277, p-value = 0.001668
cor_vel_leg <- cor.test(vel_all[Subcomponent == "Legato"]$Teaching, vel_all[Subcomponent == "Legato"]$Mean)
cor_vel_leg
##
## Pearson's product-moment correlation
##
## data: vel_all[Subcomponent == "Legato"]$Teaching and vel_all[Subcomponent == "Legato"]$Mean
## t = 0.65532, df = 46, p-value = 0.5155
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1932420 0.3701921
## sample estimates:
## cor
## 0.0961733
cor_vel_leg_spearman <- cor.test(vel_all[Subcomponent == "Legato"]$Teaching, vel_all[Subcomponent == "Legato"]$Mean, method = "spearman", exact = FALSE)
cor_vel_leg_spearman
##
## Spearman's rank correlation rho
##
## data: vel_all[Subcomponent == "Legato"]$Teaching and vel_all[Subcomponent == "Legato"]$Mean
## S = 17876, p-value = 0.8409
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.02975708
# normality check
vel_sta_norm_teaching <- shapiro.test(vel_all[Subcomponent == "Staccato"]$Teaching)
vel_sta_norm_judge <- shapiro.test(vel_all[Subcomponent == "Staccato"]$Mean)
qqnorm(vel_all[Subcomponent == "Staccato"]$Teaching)
qqline(vel_all[Subcomponent == "Staccato"]$Teaching)
vel_sta_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Staccato"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(vel_all[Subcomponent == "Staccato"]$Mean)
qqline(vel_all[Subcomponent == "Staccato"]$Mean)
vel_sta_norm_judge
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Staccato"]$Mean
## W = 0.97486, p-value = 0.3867
cor_vel_sta <- cor.test(vel_all[Subcomponent == "Staccato"]$Teaching, vel_all[Subcomponent == "Staccato"]$Mean)
cor_vel_sta
##
## Pearson's product-moment correlation
##
## data: vel_all[Subcomponent == "Staccato"]$Teaching and vel_all[Subcomponent == "Staccato"]$Mean
## t = -0.15953, df = 46, p-value = 0.8739
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3056081 0.2623724
## sample estimates:
## cor
## -0.02351532
## `geom_smooth()` using formula 'y ~ x'
# normality check
vel_diff_ftop_norm_teaching <- shapiro.test(vel_diff_all[Subcomponent == "FtoP"]$Teaching)
vel_diff_ftop_norm_judge <- shapiro.test(vel_diff_all[Subcomponent == "FtoP"]$Mean)
qqnorm(vel_diff_all[Subcomponent == "FtoP"]$Teaching)
qqline(vel_diff_all[Subcomponent == "FtoP"]$Teaching)
vel_diff_ftop_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "FtoP"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(vel_diff_all[Subcomponent == "FtoP"]$Mean)
qqline(vel_diff_all[Subcomponent == "FtoP"]$Mean)
vel_diff_ftop_norm_judge
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "FtoP"]$Mean
## W = 0.97502, p-value = 0.3919
cor_vel_diff_ftop <- cor.test(vel_diff_all[Subcomponent == "FtoP"]$Teaching, vel_diff_all[Subcomponent == "FtoP"]$Mean)
cor_vel_diff_ftop
##
## Pearson's product-moment correlation
##
## data: vel_diff_all[Subcomponent == "FtoP"]$Teaching and vel_diff_all[Subcomponent == "FtoP"]$Mean
## t = -3.8654, df = 46, p-value = 0.0003463
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6831689 -0.2455614
## sample estimates:
## cor
## -0.4951484
# normality check
vel_diff_ptof_norm_teaching <- shapiro.test(vel_diff_all[Subcomponent == "PtoF"]$Teaching)
vel_diff_ptof_norm_judge <- shapiro.test(vel_diff_all[Subcomponent == "PtoF"]$Mean)
qqnorm(vel_diff_all[Subcomponent == "PtoF"]$Teaching)
qqline(vel_diff_all[Subcomponent == "PtoF"]$Teaching)
vel_diff_ptof_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "PtoF"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(vel_diff_all[Subcomponent == "PtoF"]$Mean)
qqline(vel_diff_all[Subcomponent == "PtoF"]$Mean)
vel_diff_ptof_norm_judge
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "PtoF"]$Mean
## W = 0.97646, p-value = 0.4411
cor_vel_diff_ptof <- cor.test(vel_diff_all[Subcomponent == "PtoF"]$Teaching, vel_diff_all[Subcomponent == "PtoF"]$Mean)
cor_vel_diff_ptof
##
## Pearson's product-moment correlation
##
## data: vel_diff_all[Subcomponent == "PtoF"]$Teaching and vel_diff_all[Subcomponent == "PtoF"]$Mean
## t = 5.3289, df = 46, p-value = 2.893e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4047327 0.7672651
## sample estimates:
## cor
## 0.6178191
# normality check
vel_diff_ltos_norm_teaching <- shapiro.test(vel_diff_all[Subcomponent == "LtoS"]$Teaching)
vel_diff_ltos_norm_judge <- shapiro.test(vel_diff_all[Subcomponent == "LtoS"]$Mean)
qqnorm(vel_diff_all[Subcomponent == "LtoS"]$Teaching)
qqline(vel_diff_all[Subcomponent == "LtoS"]$Teaching)
vel_diff_ltos_norm_teaching
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "LtoS"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(vel_diff_all[Subcomponent == "LtoS"]$Mean)
qqline(vel_diff_all[Subcomponent == "LtoS"]$Mean)
vel_diff_ltos_norm_judge
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "LtoS"]$Mean
## W = 0.93052, p-value = 0.007129
cor_vel_diff_ltos <- cor.test(vel_diff_all[Subcomponent == "LtoS"]$Teaching, vel_diff_all[Subcomponent == "LtoS"]$Mean)
cor_vel_diff_ltos
##
## Pearson's product-moment correlation
##
## data: vel_diff_all[Subcomponent == "LtoS"]$Teaching and vel_diff_all[Subcomponent == "LtoS"]$Mean
## t = -1.8478, df = 46, p-value = 0.07107
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.50897866 0.02299395
## sample estimates:
## cor
## -0.2628581
cor_vel_diff_ltos_spearman <- cor.test(vel_diff_all[Subcomponent == "LtoS"]$Teaching, vel_diff_all[Subcomponent == "LtoS"]$Mean, method = "spearman", exact = FALSE)
cor_vel_diff_ltos_spearman
##
## Spearman's rank correlation rho
##
## data: vel_diff_all[Subcomponent == "LtoS"]$Teaching and vel_diff_all[Subcomponent == "LtoS"]$Mean
## S = 22664, p-value = 0.1156
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2301161
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "StoL"]$Teaching
## W = 0.9715, p-value = 0.2895
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "StoL"]$Mean
## W = 0.9637, p-value = 0.1423
##
## Pearson's product-moment correlation
##
## data: vel_diff_all[Subcomponent == "StoL"]$Teaching and vel_diff_all[Subcomponent == "StoL"]$Mean
## t = -1.28, df = 46, p-value = 0.207
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4460800 0.1041734
## sample estimates:
## cor
## -0.1854505
pcor.test(partial[Subcomponent == "Legato"]$KOT, partial[Subcomponent == "Legato"]$Teaching, partial[Subcomponent == "Legato", c("IOI", "KV", "KVDiff")])
pcor.test(partial[Subcomponent == "Staccato"]$KOT, partial[Subcomponent == "Staccato"]$Teaching, partial[Subcomponent == "Staccato", c("IOI", "KV", "KVDiff")])
pcor.test(partial[Subcomponent == "Forte"]$KV, partial[Subcomponent == "Forte"]$Teaching, partial[Subcomponent == "Forte", c("IOI", "KOT", "KVDiff")])
pcor.test(partial[Subcomponent == "Piano"]$KV, partial[Subcomponent == "Piano"]$Teaching, partial[Subcomponent == "Piano", c("IOI", "KOT", "KVDiff")])
pcor.test(partial[Subcomponent2 == "FtoP"]$KVDiff, partial[Subcomponent2 == "FtoP"]$Teaching, partial[Subcomponent2 == "FtoP", c("IOI", "KOT", "KV")])
pcor.test(partial[Subcomponent2 == "PtoF"]$KVDiff, partial[Subcomponent2 == "PtoF"]$Teaching, partial[Subcomponent2 == "PtoF", c("IOI", "KOT", "KV")])
Note: additive - no interaction considered
m1 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Legato"])
summary(m1)
##
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent ==
## "Legato"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.2033 -6.7841 -0.8213 7.4083 17.3467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -84.01813 25.74162 -3.264 0.00216 **
## IOI 0.77703 0.09796 7.932 5.92e-10 ***
## KOT 0.25905 0.08430 3.073 0.00367 **
## KV -0.16055 0.23894 -0.672 0.50523
## KVDiff -0.08689 0.23141 -0.375 0.70916
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.77 on 43 degrees of freedom
## Multiple R-squared: 0.6764, Adjusted R-squared: 0.6463
## F-statistic: 22.47 on 4 and 43 DF, p-value: 4.541e-10
check_model(m1)
m2 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Staccato"])
summary(m2)
##
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent ==
## "Staccato"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.2526 -7.8795 0.0325 6.8427 26.1285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -88.1667 23.4668 -3.757 0.000513 ***
## IOI 0.5216 0.1596 3.268 0.002136 **
## KOT -0.2754 0.1137 -2.422 0.019734 *
## KV 0.1423 0.1983 0.718 0.476815
## KVDiff -0.5957 0.2977 -2.001 0.051718 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.87 on 43 degrees of freedom
## Multiple R-squared: 0.6703, Adjusted R-squared: 0.6397
## F-statistic: 21.86 on 4 and 43 DF, p-value: 6.706e-10
check_model(m2)
m3 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Forte"])
summary(m3)
##
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent ==
## "Forte"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.635 -9.911 -0.307 8.468 35.214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -83.25224 36.29071 -2.294 0.02674 *
## IOI 0.34645 0.12283 2.821 0.00722 **
## KOT -0.05319 0.06448 -0.825 0.41398
## KV 0.72644 0.35607 2.040 0.04751 *
## KVDiff -0.60640 0.30913 -1.962 0.05630 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.84 on 43 degrees of freedom
## Multiple R-squared: 0.4136, Adjusted R-squared: 0.359
## F-statistic: 7.582 on 4 and 43 DF, p-value: 0.0001027
check_model(m3)
m4 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Piano"])
summary(m4)
##
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent ==
## "Piano"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.7853 -9.3690 0.0482 6.4645 24.9311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.70422 27.71307 -0.242 0.809996
## IOI 0.38444 0.10726 3.584 0.000857 ***
## KOT -0.04360 0.06144 -0.710 0.481697
## KV -0.67695 0.35915 -1.885 0.066221 .
## KVDiff 0.98914 0.18738 5.279 4.04e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.95 on 43 degrees of freedom
## Multiple R-squared: 0.5536, Adjusted R-squared: 0.5121
## F-statistic: 13.33 on 4 and 43 DF, p-value: 3.799e-07
check_model(m4)